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Multi-Cluster Cooperative Offloading for VR Task: A MARL Approach With Graph Embedding

IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第9期23卷 8773-8788页

作者： Yang, Yang Feng, Lei Sun, Yao Li, Yangyang Li, Wenjing Imran, Muhammad Ali Beijing Univ Posts & Telecommun BUPT State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Univ Glasgow James Watt Sch Engn Glasgow City G12 8QQ Scotland

Virtual reality (VR) technology has recently achieved notable success and been widely expected to interplay with more mobile multimedia services. To further enhance real-time immersive experience for VR applications, exploiting cooperative offloading among capable terminal devices should be emerged as an effective means. However, faced with diverse and surging mobile VR user requests, terminal-assisted offloading needs to support comprehensive cached content, ultra-low latency delivery, and continuous energy provisioning, to guarantee stringent quality of service requirements, which poses a critical challenge for resource-constrained terminals. Hence, this paper proposes a Cooperative Offloading framework for Terminal Clusters (named CO-TC), in which VR terminal clusters form several cooperation groups for sharing cached field of view (FoV) tiles and available computing resources to cooperatively perform FoV rendering and content delivery. To maximize energy efficiency in CO-TC, an optimization problem is formulated to jointly decide the task offloading and computing resource utilization. An intelligent offloading scheme is designed based on multi-agent reinforcement learning (MARL) specially using agent relation feature graph embeddings. Moreover, we theoretically prove the permutation invariance and convergence of the proposed algorithm and derive the optimal observation range of the agent to balance the performance gain and interaction overhead in the distributed MARL frame. Finally, simulation results show that the proposed offloading scheme outperforms other baselines in terms of VR service performance, including latency, energy consumption, and energy efficiency.

关键词： Task analysis Collaboration Servers Rendering (computer graphics) Resource management Heuristic algorithms Quality of service VR task offloading terminal cluster collaboration MARL graph embedding

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Delay-prioritized Task Scheduling with Load Balancing in Computing Power Networks 25

Delay-prioritized Task Scheduling with Load Balancing in Com...

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IEEE Wireless Communications and networking Conference (IEEE WCNC)

作者： Feng, Li Xie, Renchao Tang, Qinqin Huang, Tao Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Purple Mt Labs Nanjing Peoples R China

ISBN: (纸本)9798350303582;9798350303599

In the era of data-driven intelligent Internet, efficient utilization of computing power is paramount. Yet, current cloud-edge collaboration architectures face challenges with computing power isolation, adversely affecting efficiency and user experience. Computing Power Networks (CPNs) leverage networks with cloud-native applications to connect and manage resources, offering a blueprint for a collaborative computing ecosystem. In the CPN, scheduling stands as a pivotal function. However, conventional scheduling often neglects the interplay between computing and networks. To rectify this, we present a collaborative task scheduling system in CPNs that simultaneously contemplates the selections of computing nodes and network links. Aiming to maintain a balanced load for both computing and network resources, we formulate the scheduling challenge as a Constrained Markov Decision Process (CMDP). This approach focuses on optimizing both execution delay and success rate of computing tasks with load balancing constraints in CPNs. To facilitate the resolution of the CMDP, we introduce a Lyapunov-optimized Deep Reinforcement Learning (DRL) algorithm, which reconfigures the long-term constraint into immediate optimization. We provide numerical results to demonstrate the effectiveness of our suggested policy and algorithm.

关键词： Computing Power Networks collaborative task scheduling delay optimization load balancing

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An Intelligent Congestion Control Algorithm for Computing Power Network 14th

An Intelligent Congestion Control Algorithm for Computing Po...

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14th International Conference on Computer Engineering and Networks, CENet 2024

作者： Jin, Ming Li, Yuting Hao, Jiakai Piao, Tiangao Yang, Jing Chen, Jinqian State Grid Beijing Electric Power Company Beijing100031 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789819649624

Due to the randomness of traffic in the computing power network and the diversity of paths, congestion is inevitable. When network congestion occurs, it will cause increased queuing delay, low network utilization, resulting in deterioration of application performance. Nowadays, there are many congestion control methods. By continuously adjusting the transmission rate of the end side, the capacity of the incoming network is finally approached as close as possible to the carrying capacity of the network to solve the congestion problem in the network. However, the current mainstream method has the disadvantages of insufficient control precision and is incapable of self-adaptive adjustment. Therefore, it is necessary to propose a new type of intelligent congestion control algorithm based on deep reinforcement learning to achieve more active control capabilities and more precise rate regulation, and to achieve the goals of full bandwidth, low latency, fast convergence, and excellent fairness. Thereby effectively improving the transmission efficiency of the computing power network and ensuring the efficient completion of large-scale distributed computing tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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Fast and Scalable Data Plane Verification for Burst Updates With Edge-Predicate

IEEE TRANSACTIONS ON NETWORKING

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IEEE TRANSACTIONS ON networking 2025年第3期33卷 1279-1294页

作者： Wang, Jingyu Zhao, Chenyang Zhuang, Zirui Qi, Qi Guo, Yuebin Sun, Haifeng Guo, Lingqi Liao, Jianxin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Pengcheng Lab Shenzhen 518000 Peoples R China Ebyte com Beijing 100191 Peoples R China

There is an increasing interest in data plane verification, which is designed to automatically verify network correctness through directly analyzing the data plane. Recent data plane verifiers have been able to do real-time sub-millisecond per rule verification. However, we observe that in real-world networks, individual data plane updates rarely occur. On the contrary, there are always a certain number of updates generated in a short period of time, called as burst updates, due to high-level user intend or uncertain network events. When it comes to this real-life scenario, yet, the current equivalence class (EC) based methods are unable to solve the model-wide changes problem caused by the EC itself, which significantly slows down the verification speed of burst updates. To overcome this limitation, we present EPVerifier, a fast, scalable data plane verifier accelerating burst updates verification with edge-predicate (EP). Instead of classifying packets into ECs according to global forwarding behavior, the EPVerifier uses one EP per edge to represent all packets that can pass through. Furthermore, with EPs that clearly have localized properties, we introduce a rule type extension that does not require a change in the granularity of the network model to support ACLs and NATs that are prevalent in real devices, and obtain better-performing parallelism by dividing the verification task based on switches. Experiments on both dataset simulations and real-life deployments show that EPVerifier achieves 2-10x faster data plane verification than the state-of-the-art and such advantage expand with the data plane's complexity and update scale growth.

关键词： Data models Telecommunications Switches Firewalls (computing) Sun Process control Optimization Directed graphs Telecommunication traffic Technological innovation Automated tools formal methods network management data plane verification burst updates

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FAT: FIELD-AWARE TRANSFORMER FOR 3D POINT CLOUD SEMANTIC SEGMENTATION 30

FAT: FIELD-AWARE TRANSFORMER FOR 3D POINT CLOUD SEMANTIC SEG...

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30th IEEE International Conference on Image Processing (ICIP)

作者： Zhou, Junjie Xiong, Yongping Chiu, Chinwai Liu, Fangyu Gong, Xiangyang Beijing Univ Posts & Telecommun Sch Comp Sci State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9781728198354

Transformer models have achieved promising performances in point cloud segmentation. However, most existing attention schemes provide the same feature learning paradigm for all points equally and overlook the enormous difference in size among scene objects. In this paper, we propose the Field-Aware Transformer (FAT) that adjusts the attentive receptive fields for objects of different sizes. Our FAT achieves field-aware learning via two steps: introduce multi-granularity features to each attention layer and allow each point to choose its attentive fields adaptively. It contains two key designs: the Multi-Granularity Attention (MGA) scheme and the Re-Attention module. Extensive experimental results demonstrate that FAT achieves state-of-the-art performances on S3DIS [1] and ScanNetV2 [2] datasets.

关键词： Point Cloud Semantic Segmentation Transformer Multi-Granularity Attention Receptive Field

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Environment Information-Based Channel Prediction Method Assisted by Graph Neural Network

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China Communications 2022年第11期19卷 1-15页

作者： Yutong Sun Jianhua Zhang Yuxiang Zhang Li Yu Qixing Wang Guangyi Liu State Key Lab of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Future Research Laboratory China Mobile Research InstituteBeijing 100053China

Recently,whether the channel prediction can be achieved in diverse communication scenarios by directly utilizing the environment information gained lots of attention due to the environment impacting the propagation characteristics of the wireless *** paper presents an environment information-based channel prediction(EICP)method for connecting the environment with the channel assisted by the graph neural networks(GNN).Firstly,the effective scatterers(ESs)producing paths and the primary scatterers(PSs)generating single propagation paths are detected by building the scatterercentered communication environment graphs(SCCEGs),which can simultaneously preserve the structure information and highlight the pending *** GNN-based classification model is implemented to distinguish ESs and PSs from other ***,large-scale parameters(LSP)and small-scale parameters(SSP)are predicted by employing the GNNs with multi-target architecture and the graphs of detected ESs and *** results show that the average normalized mean squared error(NMSE)of LSP and SSP predictions are 0.12 and 0.008,which outperforms the methods of linear data learning.

关键词： channel prediction propagation environment graph scatterer detection GNN

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A Hybrid Federated Learning Framework for Task-Oriented Semantic Communication

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IEEE Internet of Things Journal 2025年第13期12卷 23444-23461页

作者： Sun, Haofeng Ni, Wanli Tian, Hui Zheng, Jingheng Nie, Gaofeng Niyato, Dusit Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Tsinghua University Department of Electronic Engineering Beijing100084 China Nanyang Technological University College of Computing and Data Science 117583 Singapore

In existing deep learning-based semantic communication systems, centralized training of semantic models brings a risk of privacy leakage, whereas distributed training imposes a huge computational burden on user equipments (UEs). To address these challenges, we propose a hybrid federated learning (Hybrid-FL) framework to alleviate the computational burden on UEs while protecting the user privacy. Specifically, each UE uploads local gradients and semantic symbols to the base station for the collaborative training of global and local semantic models. Furthermore, we propose a joint communication and computation scheme for supporting the model aggregation and semantics transmission. To gain deep insights, we expose the joint impact of communication and computation on the convergence behavior of Hybrid-FL by deriving an upper bound. Then, we formulate a mixed-integer non-linear programming problem to improve the convergence performance of Hybrid-FL, which is then effectively solved by using our proposed algorithm that developed based on alternating and matching theory. Experimental results demonstrate that Hybrid-FL outperforms conventional FL by achieving a 20% accuracy gain and a 80% latency reduction. © 2014 IEEE.

关键词： Federated learning

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Practical efficient 1-out-of-n quantum oblivious transfer protocol

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QUANTUM INFORMATION PROCESSING 2023年第2期22卷 99-99页

作者： Zhang, Xue Wei, Chunyan Qin, Sujuan Gao, Fei Wen, Qiaoyan Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Luoyang Normal Univ Coll Math Sci Luoyang 471934 Peoples R China

1-out-of-n oblivious transfer is an important research topic in secure multiparty computation, including its quantum version, i.e., quantum oblivious transfer (QOT). However, most QOT protocols (including its most practical variant called QKD-based quantum private query) cannot efficiently send the receiver exact one bit from the n bits transmitted by the sender. These protocols' communication complexity is usually O(nlogn). Since modern data size can be extremely large, it is important to reduce this communication as much as possible. In this sense, we propose a more efficient 1-out-of-n QOT protocol, whose communication complexity is O(logn). A potential loophole in our protocol is part of transmitted bits may be partial correlated. We then give an improved protocol to eliminate their correlation with communication complexity of O(n). Besides, our protocol has better performance in resisting the joint-measurement attack.

关键词： Quantum cryptography Quantum oblivious transfer Joint-measurement attack

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Adaptive Hardcore Bit and Quantum key Leasing over Classical Channel from LWE with Polynomial Modulus 30th

Adaptive Hardcore Bit and Quantum Key Leasing over Classical...

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30th International Conference on the Theory and Application of Cryptology and Information Security

作者： Duong Hieu Phan Wen, Weiqiang Yan, Xingyu Zheng, Jinwei Telecom Paris LTCI Inst Polytech Paris Paris France Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9789819609468;9789819609475

Quantum key leasing, also known as public key encryption with secure key leasing (PKE-SKL), allows a user to lease a (quantum) secret key to a server for decryption purpose, with the capability of revoking the key afterwards. In the pioneering work by Chardouvelis et al. (arXiv:2310.14328), a PKE-SKL scheme utilizing classical channels was successfully built upon the noisy trapdoor claw-free (NTCF) family. This approach, however, relies on the superpolynomial hardness of learning with errors (LWE) problem, which could affect both efficiency and security of the scheme. In our work, we demonstrate that the reliance on superpolynomial hardness is unnecessary, and that LWE with polynomial-size modulus is sufficient to achieve the same goal. Our approach enhances both efficiency and security, thereby improving the practical feasibility of the scheme on near-term quantum devices. To accomplish this, we first construct a noticeable NTCF (NNTCF) family with the adaptive hardcore bit property, based on LWE with polynomial-size modulus. To the best of our knowledge, this is the first demonstration of the adaptive hardcore bit property based on LWE with polynomial-size modulus, which may be of independent interest. Building on this foundation, we address additional challenges in prior work to construct the first PKE-SKL scheme satisfying the following properties: ( i) the entire protocol utilizes only classical communication, and can also be lifted to support homomorphism. (ii) the security is solely based on LWE assumption with polynomial-size modulus. As a demonstration of the versatility of our noticeable NTCF, we show that an efficient proof of quantumness protocol can be built upon it. Specifically, our protocol enables a classical verifier to test the quantumness while relying exclusively on the LWE assumption with polynomialsize modulus.

关键词： Trapdoor claw-free functions Adaptive hardcore bit Secure key leasing Proofs of quantumness Learning with errors

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Practical Private Aggregation in Federated Learning Against Inference Attack

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IEEE INTERNET OF THINGS JOURNAL 2023年第1期10卷 318-329页

作者： Zhao, Ping Cao, Zhikui Jiang, Jin Gao, Fei Donghua Univ Coll Informat Sci & Technol Shanghai 200051 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Federated learning (FL) enables multiple worker devices share local models trained on their private data to collaboratively train a machine learning model. However, local models are proved to imply the information about the private data and, thus, introduce much vulnerabilities to inference attacks where the adversary reconstructs or infers the sensitive information about the private data (e.g., labels, memberships, etc.) from the local models. To address this issue, existing works proposed homomorphic encryption, secure multiparty computation (SMC), and differential privacy methods. Nevertheless, the homomorphic encryption and SMC-based approaches are not applicable to large-scale FL scenarios as they incur substantial additional communication and computation costs and require secure channels to delivery keys. Moreover, differential privacy brings a substantial tradeoff between privacy budget and model performance. In this article, we propose a novel FL framework, which can protect the data privacy of worker devices against the inference attacks with minimal accuracy cost and low computation and communication cost, and does not rely on the secure pairwise communication channels. The main idea is to generate the lightweight keys based on computational Diffie-Hellman (CDH) problem to encrypt the local models, and the FL server can only get the sum of the local models of all worker devices without knowing the exact local model of any specific worker device. The extensive experimental results on three real-world data sets validate that the proposed FL framework can protect the data privacy of worker devices, and only incurs a small constant of computation and communication cost and a drop in test accuracy of no more than 1%.

关键词： Computational modeling Costs Data models Servers Training Homomorphic encryption Differential privacy Computational Diffie--Hellman (CDH) data privacy federated learning (FL) inference attacks

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